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Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium

Publication Year :
2020

Abstract

Based on administrative data of unemployed in Belgium, we estimate the labour market effects of three training programmes at various aggregation levels using Modified Causal Forests, a causal machine learning estimator. While all programmes have positive effects after the lock-in period, we find substantial heterogeneity across programmes and unemployed. Simulations show that “black-box” rules that reassign unemployed to programmes that maximise estimated individual gains can considerably improve effectiveness: up to 20% more (less) time spent in (un)employment within a 30 months window. A shallow policy tree delivers a simple rule that realizes about 70% of this gain.

Details

Language :
English
Database :
OpenAIRE
Accession number :
edsair.dris...00893..06a347577568ab4c43f145c89090018f